# Using Active Learning Methods to Strategically Select Essays for Automated Scoring

arXiv: https://arxiv.org/abs/2301.00628 · HTML: https://mlsanity.com/paper/2301.00628/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Topology-Imbalance Learning for Semi-Supervised Node Classification](https://mlsanity.com/paper/2110.04099/) (2021)
- [TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification](https://mlsanity.com/paper/2206.12917/) (2022)
- [Unsupervised Pool-Based Active Learning for Linear Regression](https://mlsanity.com/paper/2001.05028/) (2020)
- [Active Learning for Automated Visual Inspection of Manufactured Products](https://mlsanity.com/paper/2109.02469/) (2021)
- [Effective sampling for large-scale automated writing evaluation systems](https://mlsanity.com/paper/1412.5659/) (2014)

## Built on by (most cited first, 1 of 1)

- [Enhancing Marker Scoring Accuracy through Ordinal Confidence Modelling in Educational Assessments](https://mlsanity.com/paper/2505.23315/) (2025)

## Abstract (arXiv)

Research on automated essay scoring has become increasing important because it serves as a method for evaluating students' written-responses at scale. Scalable methods for scoring written responses are needed as students migrate to online learning environments resulting in the need to evaluate large numbers of written-response assessments. The purpose of this study is to describe and evaluate three active learning methods than can be used to minimize the number of essays that must be scored by human raters while still providing the data needed to train a modern automated essay scoring system. The three active learning methods are the uncertainty-based, the topological-based, and the hybrid method. These three methods were used to select essays included as part of the Automated Student Assessment Prize competition that were then classified using a scoring model that was training with the bidirectional encoder representations from transformer language model. All three active learning methods produced strong results, with the topological-based method producing the most efficient classification. Growth rate accuracy was also evaluated. The active learning methods produced different levels of efficiency under different sample size allocations but, overall, all three methods were highly efficient and produced classifications that were similar to one another.